fix(logging): report the cost-map entry billing used in model_map_information (#47182)

Backport of #47182 to stable/1.106.x.
Cherry-picked from 1212260ab1 (main).
Test conflict resolved by keeping the line's file plus this PR's own tests.

litellm/litellm_core_utils/litellm_logging.py

@@ -6023,6 +6023,7 @@ class StandardLoggingPayloadSetup:
         custom_llm_provider: str | None,
         init_response_obj: object,
         api_base: str | None = None,
+        router_model_id: str | None = None,
     ) -> StandardLoggingModelInformation:
         model_cost_name: Final = select_model_name_for_cost_calc(
             model=base_model if custom_pricing else None,
@@ -6030,12 +6031,23 @@ class StandardLoggingPayloadSetup:
             base_model=base_model,
             custom_pricing=custom_pricing,
         )
+        pricing_model_name: Final = (
+            select_model_name_for_cost_calc(
+                model=base_model if custom_pricing else None,
+                completion_response=init_response_obj,
+                base_model=base_model,
+                custom_pricing=custom_pricing,
+                router_model_id=router_model_id,
+            )
+            if router_model_id is not None
+            else model_cost_name
+        )
         if model_cost_name is None:
             model_cost_information = StandardLoggingModelInformation(model_map_key="", model_map_value=None)
         else:
             try:
                 _model_cost_information: Final = litellm.get_model_info(
-                    model=model_cost_name,
+                    model=pricing_model_name or model_cost_name,
                     custom_llm_provider=custom_llm_provider,
                     api_base=api_base,
                 )
@@ -6714,6 +6726,7 @@ def get_standard_logging_object_payload(
             custom_llm_provider=kwargs.get("custom_llm_provider"),
             init_response_obj=init_response_obj,
             api_base=litellm_params.get("api_base"),
+            router_model_id=logging_obj.get_router_model_id(),
         )
 
         error_information, error_str = StandardLoggingPayloadSetup.get_error_information_for_logging_payload(

tests/unit/litellm_core_utils/test_litellm_logging.py

@@ -38,6 +38,7 @@ from litellm.litellm_core_utils.litellm_logging import (
     StandardLoggingPayloadSetup,
     _extract_response_obj_and_hidden_params,
     _get_status_fields,
+    get_standard_logging_object_payload,
     set_callbacks,
 )
 from litellm.litellm_core_utils.logging_worker import GLOBAL_LOGGING_WORKER
@@ -11563,3 +11564,86 @@ class CompletionCustomHandler(
         except Exception:
             print(f"Assertion Error: {traceback.format_exc()}")
             self.errors.append(traceback.format_exc())
+
+
+@pytest.mark.parametrize(
+    ("input_cost_per_token", "output_cost_per_token"),
+    [(1e-06, 2e-06), (0.0, 0.0)],
+    ids=["custom_price", "explicit_zero_price"],
+)
+def test_standard_logging_payload_reports_the_deployment_entry_the_request_was_billed_from(
+    input_cost_per_token: float, output_cost_per_token: float
+):
+    """A deployment with its own price keeps the catalog model as model_map_key but is logged at its own rates."""
+    deployment_id: Final = "lit9660-priced-deployment"
+    deployment_model_info: Final = {
+        "id": deployment_id,
+        "input_cost_per_token": input_cost_per_token,
+        "output_cost_per_token": output_cost_per_token,
+    }
+    litellm.register_model(
+        model_cost={
+            deployment_id: {
+                "litellm_provider": "openai",
+                "mode": "chat",
+                "input_cost_per_token": input_cost_per_token,
+                "output_cost_per_token": output_cost_per_token,
+            }
+        },
+        persist_across_reloads=False,
+    )
+    try:
+        logging_obj: Final = LitellmLogging(
+            model="gpt-5.4-nano",
+            messages=[{"role": "user", "content": "Hi"}],
+            stream=False,
+            call_type="completion",
+            start_time=time.time(),
+            litellm_call_id="lit9660",
+            function_id="fn",
+        )
+        litellm_params: Final = {
+            "metadata": {
+                "model_group": "lit9660-group",
+                "deployment": "openai/gpt-5.4-nano",
+                "model_info": deployment_model_info,
+            }
+        }
+        logging_obj.update_environment_variables(
+            model="gpt-5.4-nano", user="", optional_params={}, litellm_params=litellm_params, custom_llm_provider="openai"
+        )
+        response: Final = ModelResponse(
+            model="gpt-5.4-nano",
+            choices=[litellm.Choices(message=litellm.Message(role="assistant", content="hi"))],
+            usage=litellm.Usage(prompt_tokens=10, completion_tokens=6, total_tokens=16),
+        )
+
+        response_cost: Final = logging_obj.response_cost_calculator(result=response)
+        payload: Final = get_standard_logging_object_payload(
+            kwargs={
+                "model": "gpt-5.4-nano",
+                "custom_llm_provider": "openai",
+                "litellm_params": litellm_params,
+                "response_cost": response_cost,
+            },
+            init_response_obj=response,
+            start_time=datetime.datetime.now(),
+            end_time=datetime.datetime.now(),
+            logging_obj=logging_obj,
+            status="success",
+        )
+
+        assert response_cost == 10 * input_cost_per_token + 6 * output_cost_per_token
+        assert payload is not None
+        assert payload["model_map_information"] == {
+            "model_map_key": "openai/gpt-5.4-nano",
+            "model_map_value": litellm.get_model_info(model=f"openai/{deployment_id}", custom_llm_provider="openai"),
+        }
+        model_map_value: Final = payload["model_map_information"]["model_map_value"]
+        assert model_map_value is not None
+        assert (model_map_value["input_cost_per_token"], model_map_value["output_cost_per_token"]) == (
+            input_cost_per_token,
+            output_cost_per_token,
+        )
+    finally:
+        litellm.model_cost.pop(deployment_id, None)